Provides functions to apply pooling or backward selection
for logistic or Cox regression prediction models in multiple imputed
datasets. Backward selection can be done from the pooled model using
Rubin's Rules (RR), the total covariance matrix (D1 method), pooling
chi-square values (D2 method), pooling likelihood ratio statistics
(D3) or pooling the median p-values. The model can contain
continuous, dichotomous, categorical predictors and interaction terms
between all type of these predictors. Continuous predictors can also
be introduced as restricted cubic spline coefficients. It is also possible
to force (spline) predictors or interaction terms in the model during predictor
selection. The package also contains functions to generate apparent model performance
measures over imputed datasets as ROC/AUC, R-squares, fit test values and
calibration plots. A wrapper function over Frank Harrell's validate function
is used for that. Bootstrap internal validation is performed in each imputed dataset
and results are pooled. Backward selection as part of internal validation
is optional and recommended. Also a function to externally validate logistic
prediction models in multiple imputed datasets is available.
Eekhout (2017)